从张量列表创建参差不齐的张量 [英] Creating a ragged tensor from a list of tensors

问题描述

我想从 TensorFlow 2.0 中的张量列表中创建一个参差不齐的张量，如下所示:

I want to create a ragged tensor from a list of tensors in TensorFlow 2.0, something like this:

a = tf.convert_to_tensor([1,2])
b = tf.convert_to_tensor([1,2,3])
tf.ragged.constant([a, b])

但这会抛出ValueError: TypeError: Scalar tensor has no `len()`.另一方面，以下代码从列表列表中创建一个参差不齐的张量，效果很好.

But this throws ValueError: TypeError: Scalar tensor has no `len()`. On the other hand, the following code, which creates a ragged tensor from a list of lists, works just fine.

a = [1,2]
b = [1,2,3]
tf.ragged.constant([a,b])

有没有办法直接从张量列表中创建一个参差不齐的张量，而无需先将张量转换为 python 列表?

Is there any way to create a ragged tensor directly from a list of tensors without first converting the tensors into python lists?

推荐答案

您可以使用 tf.RaggedTensor.对于您的情况，您可以使用例如 from_row_lengths:

You can construct ragged tensors with the different from_* methods in tf.RaggedTensor. For your case, you can use for example from_row_lengths:

import tensorflow as tf

def stack_ragged(tensors):
    values = tf.concat(tensors, axis=0)
    lens = tf.stack([tf.shape(t, out_type=tf.int64)[0] for t in tensors])
    return tf.RaggedTensor.from_row_lengths(values, lens)

a = tf.convert_to_tensor([1, 2])
b = tf.convert_to_tensor([1, 2, 3])
print(stack_ragged([a, b]))
# <tf.RaggedTensor [[1, 2], [1, 2, 3]]>

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